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Order and Prints Algorithms

summarise_rates.Rd

This function computes summaries of the estimated PDD rates, stratified by diagnostic algorithm.

Usage

summarise_rates(d0, vars, descending = TRUE, plot = TRUE)

Arguments

d0

A list with PDD data generated via diagnose_pdd_sample.

vars

A data.frame, tibble, or matrix with in the following order:

  1. variable names,

  2. variable labels

  3. type of variable (continuous, binary, or nominal)

  4. optional, group,

  5. optional, mapping each label to its description in the table’s note.

Alternatively, a path to a CSV file (semicolon-delimited) containing such a table.

descending

A logical indicating whether algorithms with the highest estimated PDD rates should be listed first (default is TRUE).

plot

A logical indicating whether to plot PDD rates as densities. The code is dataset-specific (default is TRUE).

Value

A list with:

table

A tibble containing raw summaries of PDD rates.

plot

A ggplot2 object visualising estimated PDD rates.

gtable

A named list of gt tables:

gtab_rates

An APA-style gt table summarising the results.

gtab_algos

An APA-style gt table listing algorithms used.

See also

diagnose_pdd_sample() prepares d0.

Examples

if (FALSE) { # \dontrun{
p <- data_paths("data-raw")
data <- prepare_data(p)
pdd  <- diagnose_pdd_sample(data)
vars <- here::here("data-raw", "VariablesOfInterest.csv")

rates0 <- summarise_rates(pdd, vars)
rates1 <- summarise_rates(pdd, vars, TRUE)  # rates0 and rates1 are identical
rates2 <- summarise_rates(pdd, vars, FALSE) # ascending order
} # }